Domain terminology expansion by relevancy

ABSTRACT

Methods, computer program products, and systems are presented. The methods include, for instance: collecting various word data from cross-domain sources and subject websites; assessing relevancy of feature vectors from external domains, live content of subject websites, and secondary terms derived from the live contents; expanding a language model for a domain by relevance passing a logistic regression threshold.

TECHNICAL FIELD

The present disclosure relates to domain language modeling technology,and more particularly to methods, computer program products, and systemsfor improving accuracy and range of domain corpus.

BACKGROUND

In conventional language modeling, measuring a probability of a wordsequence to be within a context is based on n-grams frequency counts. Inadapting a language model to a domain, the meaning of word sequence maybecome ambiguous and accordingly may result in inaccurate language modelfor the domain.

SUMMARY

The shortcomings of the prior art are overcome, and additionaladvantages are provided, through the provision, in one aspect, of amethod. The method for expanding a language model corresponding to adomain includes, for example: collecting, by one or more processor, atleast one feature vector from one or more external domain distinctivefrom the domain, wherein the domain and the one or more external domainare interconnected via a cloud; expanding the language model, stored ina corpora coupled to the cloud, with a feature vector of the at leastone feature vector from the collecting, wherein the feature vector is,based on a logistic regression threshold, more relevant to the domainthan not; and enhancing the language model by machine learning livecontent from one or more subject website in which the domain isinterested such that the language model may include the live content andone or more secondary term derived from the live content that are morerelevant to the domain than not, such that the language model accuratelyand comprehensively facilitate an automatic speech recognition (ASR)system for the domain.

Additional features are realized through the techniques set forthherein. Other embodiments and aspects, including but not limited tocomputer program product and system, are described in detail herein andare considered a part of the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more aspects of the present invention are particularly pointedout and distinctly claimed as examples in the claims at the conclusionof the specification. The foregoing and other objects, features, andadvantages of the invention are apparent from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 depicts a system for coerced expansion of domain terminology, inaccordance with one or more embodiments set forth herein;

FIG. 2 depicts a flowchart performed by the domain terminology expansionprocess, in accordance with one or more embodiments set forth herein;

FIG. 3 depicts a detailed flowchart of the speech-to-text onlinelearning pipeline, in accordance with one or more embodiments set forthherein;

FIG. 4 depicts a detailed flowchart of the inclusion test based onlogistic regression, in accordance with one or more embodiments setforth herein;

FIG. 5 depicts a cloud computing node according to an embodiment of thepresent invention;

FIG. 6 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 7 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

FIG. 1 depicts a system 100 for coerced expansion of domain terminology,in accordance with one or more embodiments set forth herein.

The system 100 includes multiple domains 111, 113, 115, and a languagemodeling process 170 interconnected via a cloud 110. In thisspecification, a domain that invokes the language modeling process 170is referred to as a target domain, which is selected from the multipledomains 111, 113, and 115. From the perspective of the target domain,the rest of domains are referred to as external domains.

The system 100 adapts a language model to the target domain of themultiple domains, 111, 113, and 115, as well as expands a domainterminology by selectively including words from other domains and/orlive content, which is driven by the respective relevancy of each wordto the domain terminology of the target domain. A word is coerced intothe domain terminology of the target domain, represented by a domainlanguage model 160, if the word has a relevancy higher than apreconfigured threshold with the target domain. Accordingly, worddisambiguation is also achieved by relevancy assessment and coercedexpansion of the domain terminology. Consequently, the domain languagemodel 160 and the domain terminology represented by the domain languagemodel 160 would be comprehensive in extent and accurate in applicabilityfor the target domain, and would be able to more effectively facilitatean Automatic Speech Recognition (ASR) system for the target domain thanconventional language model and domain terminologies.

Domain R 111, Domain S 113, and Domain T 115 represent respective domainspecifying a subject field of interest, such as tennis, golf, food,politics, etc. Domain R 111, Domain S 113, and Domain T 115 may includestatic domain dictionaries 112, 114, 116, respectively, that defineterms in the context of the respective subject field for each domainsuch as a tennis dictionary, golf glossaries, food terms, politicsjargons, etc. Each of the multiple domains 111, 113, 115 mayindependently utilize the language modeling process 170 in order toadapt respective language model to each domain and to expand respectivedomain terminology, which is represented as respective domain corpus inthe corpora 151.

The language modeling process 170 represents elements necessary topractice coerced expansion of domain terminology for the target domainas described in this specification. The language modeling process 170includes a domain terminology expansion process 120, a corpora 151,utility processes such as a live content crawler 153, a relevancyassessment process 155, natural language processing (NLP) components157, and the Natural Language Toolkit (NLTK) 159. The language modelingprocess 170 further includes a domain language model 160 and trainingdata 165.

The domain language model 160 corresponds to the target domain. Thedomain language model 160 is a probabilistic language model that isadapted to the target domain. Each word in the domain language model 160is associated with a respective conditional probability for the word tobe meaningful in the domain terminology, which is estimated based on asequence of preceding words. The domain language model 160 is a dynamiclanguage model which dynamically modifies conditional probabilities ofrespective words depending on a recent word history.

A word sequence, indicating a sequence of one or more word, may beincluded in more than one domain terminology wherein the word sequencehas respective domain-dependent meanings. For example, a word sequence“get an ace”, or “ace”, may be included in Tennis corpus as the wordsequence may be a tennis term which has a specific meaning within thecontext of tennis. The same word sequence “get an ace”, or “ace”, may beincluded in Cards corpus and Golf corpus as the word sequence may haverespectively specific meanings within the context of card games and thecontext of golf, respectively.

The domain terminology expansion process 120 expands a target domainterminology, indicating the domain terminology of the target domain. Thetarget domain terminology is stored in a target domain corpus, which isa domain corpus corresponding to the target domain. The domainterminology expansion process 120 collects live content from subjectdata sources respective to the target domain in expanding the targetdomain terminology. The domain terminology expansion process 120 mayfurther use word sequences from external domain corpuses correspondingto domain terminologies respective to the external domains in expandingthe target domain corpus if the word sequences are determined to berelevant to the target domain terminology. The domain terminologyexpansion process 120 includes a speech-to-text online learning pipelineprocess 130 and an inclusion test process 140. Details on operations ofthe domain terminology expansion process 120 are presented in FIG. 2 andcorresponding description. The speech-to-text online learning pipelineprocess 130 is a process progressively building up and enriching adomain terminology by use of natural language processing and machinelearning. Details on operations of the speech-to-text online learningpipeline process 130 are presented in FIG. 3 and correspondingdescription. The inclusion test process 140 systematically determineswhether or not to include certain content in the domain terminology.Details on operations of the inclusion test process 140 are presented inFIG. 4 and corresponding description.

The corpora 151 of the language modeling process 170 indicates a groupof domain corpuses that represents respective domain terminologyspecific to respective domain. In certain embodiments of the presentinvention, the corpora 151 may be implemented by use of an externalrelational database system. The corpora 151, as well as the utilityprocesses 153, 155, 157, and 159, may be independently accessed bymultiple threads of the domain terminology expansion process 120.

The live content crawler 153 of the language modeling process 170performs data mining on live content of subject sites that areassociated with the target domain. The live content crawler 153 ispreconfigured to extract specific features associated with the targetdomain from the live content, which are conducive to expand a targetcorpus corresponding to the target domain. Examples of live contentsubject to crawling may include, but are not limited to, motion data,streaming data, live feed from events, etc., which are constantlyupdated at the subject sites. In case of the target domain for aspecific sports event, examples of the live content may include, but arenot limited to, status and biographical information on players,acronyms, game rules and glossaries, articles, interview clips, livefeed of a game, manually corrected Web Video Text Tracks (WebVTT), orsimilar data that are updated as necessary to keep the content of thewebsite up-to-date. The live content crawler 153 functions as aconventional web spiders or web robots that automatically andsystematically browses interested web sites. Certain embodiments of thepresent invention implements the live content crawler 153 in the Python®programming language. (Python is a registered trademark of the PythonSoftware Foundation in the United States and other countries.)

The relevancy assessment process 155 of the language modeling process170 assesses relevancy of subject data to the target domain corpusbefore, during and after lexical analysis of the subject data. Datasubject to the relevancy assessment may include the live contentcollected by use of the live content crawler 153 and relevant wordsthereof as expanded by the domain terminology expansion process 120. Therelevancy assessment process 155 may be pre-trained by the static domaindictionaries, 112, 114, and 116, of each domain to determine respectiverelevancies of the subject data in the speech-to-text online trainingprocess 130.

In certain embodiments of the present invention, the relevancyassessment process 155 is implemented by use of an external retrievaland assessment service in order to determine how relevant a word is tothe target domain terminology, as represented in the target languagemodel. In the same embodiment, within the speech-to-text online learningpipeline 130, the same external retrieval and assessment service alsotrains the domain language model 160 for the target domain by machinelearning in order to rank terms of the target domain based on therespective relevancies of the terms. The same external retrieval andassessment service subsequently evaluates the domain language model 160for the target domain within the same speech-to-text online learningpipeline 130.

The natural language processing (NLP) component 157 of the languagemodeling process 170 collectively refers to numerous natural languageprocessing (NLP) services such as language detection and grammar check,language translation, and speech to text conversion and vice versa, etc.In certain embodiments of the present invention, the NLP component 157is implemented by use of numerous customary NLP tools as well asexternal NLP tools.

The Natural Language Toolkit (NLTK) 159 of the language modeling process170 is a publicly available, open-source suite of libraries and programsfor symbolic and statistical natural language processing (NLP) forEnglish written in the Python programming language. In certainembodiments of the present invention, the domain terminology expansionprocess 120 utilizes a part-of-speech (POS) tagger, WordNet, VerbNet,etc., in order to discover related terms derived from the live contentsuch that the related terms may be individually examined for relevancyin determining whether or not to include the related terms in the targetdomain terminology.

The training data 165 is generated from all subject data examined by thedomain terminology expansion process 120, in order to train the domainlanguage model 160. If the subject data is determined to be relevantenough to be in the domain language model 160, then the subject data istagged as an evidence supporting the domain language model 160 for thetarget domain. If the subject data is determined not to be relevantenough to be in the domain language model 160, then the subject data istagged as an evidence negating the domain language model 160 for thetarget domain.

FIG. 2 depicts a flowchart performed by the domain terminology expansionprocess 120 of FIG. 1, in accordance with one or more embodiments setforth herein.

The domain terminology expansion process 120 is initiated by thelanguage modeling process 170 for the target domain of the system 100,in order to expand a domain terminology corresponding to the targetdomain. Multiple threads of the domain terminology expansion process 120may be concurrently operational by respective domains.

In one embodiment of the present invention, the target domain is TennisDomain, and external domains, on the cloud 110 may be Golf Domain, FoodDomain, and Politics Domain, with respectively corresponding domainterminologies.

In block 210, the domain terminology expansion process 120 sendsrequests to the external domains for one or more document that may berelevant to the target domain 115. The one or more document respectivelyincludes at least one word sequence indicating a meaningful object withrespect to the external domains. Such word sequence is referred to as“feature vector” in n-gram language modeling, pattern recognition andmachine learning. Then the domain terminology expansion process 120proceeds with block 220.

Responsive to the request from block 210, each external domain on thecloud 110 performs the speech-to-text online learning pipeline of FIG. 3in block 220. As a result, external domains have respective domainterminology expanded and training data have been amplified. Details onoperations of the speech-to-text online learning pipeline are presentedin FIG. 3 and corresponding description. Subsequent to all externaldomains reporting feature vectors to the target domain, the domainterminology expansion process 120 proceeds with block 230.

In block 230, the domain terminology expansion process 120 receives thefeature vectors reported from the external domains as a result ofrunning the speech-to-text online learning pipeline, respectively. Thereceived feature vectors are meaningful for respective external domains,but may or may not be relevant to the target domain. Then the domainterminology expansion process 120 proceed with block 240.

In block 240, the domain terminology expansion process 120 performs aninclusion test, in which the domain terminology expansion process 120determines whether or not to include the feature vectors received inblock 230 in the target domain terminology based on the presentconfiguration. Details on procedure of the inclusion test are presentedin FIG. 4 and corresponding description. At the conclusion of block 240,the target terminology may be expanded with feature vectors from theexternal domains that had passed the inclusion test. Feature vectorsthat are not relevant enough for the target domain would be includedonly in the respective external domain as being domain dependent. Thenthe domain terminology expansion process 120 proceed with block 250.

In block 250, the domain terminology expansion process 120 performs thespeech-to-text online learning pipeline of FIG. 3 for the target domainsuch that the target domain further expand the target domain terminologywith live content collected on-line. Then the domain terminologyexpansion process 120 concludes one cycle of the domain terminologyexpansion operation. The domain terminology expansion process 120 mayiterate blocks 210 through 250 as necessary.

FIG. 3 depicts a detailed flowchart of the speech-to-text onlinelearning pipeline as performed in blocks 220 and 250 of FIG. 2, inaccordance with one or more embodiments set forth herein.

In block 310, the domain terminology expansion process 120 creates arelevancy model based on static dictionary data of a current domain andtrains the relevancy assessment process 155 of FIG. 1 forassessing/ranking relevancies of word sequences to the current domain.The relevancy model represents linguistic ontology determining whetheror not a certain word sequence constitutes recognizable language of thecurrent domain and/or lexicon of the current domain. The relevancy modelis utilized in assessing relevancy of all word sequences to domainterminology of the current domain, or in ranking the word sequences inorder of relevancies. Then the domain terminology expansion process 120proceeds with block 320.

In the same embodiment of the present invention wherein the targetdomain is Tennis Domain, the static dictionary data of a current domaincorresponds to a Tennis Dictionary in block 250. In block 220, thestatic dictionary data of a current domain corresponds to respectivedictionaries of each external domain such as a Golf Dictionary, aCulinary Dictionary, a Politics Dictionary, etc. In certain embodimentsof the present invention wherein the relevancy assessment process 150 ofFIG. 1 is implemented with an external retrieval and assessment service,the relevancy model generated from the static dictionary data of thecurrent domain is provided to the same external retrieval and assessmentservice as input in block 310.

In block 320, the domain terminology expansion process 120 collects livecontent from subject websites by crawling. The domain terminologyexpansion process 120 detects language of the live content as the domainterminology expansion process 120 supports a preapproved language only.If preconfigured and supported, the domain terminology expansion process120 may automatically translate unsupported language to a supportedlanguage in order to further process the live content. The domainterminology expansion process 120 may screen grammar of the live contentsuch that only meaningful content would be stored the live content inthe corpora as a corpus for the current domain. Then the domainterminology expansion process 120 proceeds with block 330.

In the same embodiment of the present invention wherein the targetdomain is Tennis Domain, the subject website to be crawled may be awebsite for any world-renowned tennis tournament. The domain terminologyexpansion process 120 supports only English, and may automaticallytranslate German and French content to English for further processingprior to storing the content to the Tennis corpus.

In block 330, the domain terminology expansion process 120 assessesrelevancy of the live content to the domain terminology. Then the domainterminology expansion process 120 proceeds with block 340.

Prior to assessing relevancy of each word sequence, the domainterminology expansion process 120 calculates a term frequency-inversedocument frequency (tf-idf) metric to reflect how important a word is toa document in the domain corpus, based on the static dictionary data ofthe current domain running the speech-to-text online learning pipeline.A retrieval and assessment service employed for certain embodiments ofthe present invention processes and expands the live content, extracts alanguage model, and builds ground truths to train a ranker for thecurrent domain.

The domain terminology expansion process 120 performs blocks 340 through370 as a unit for each sentence in the live content collected from block320. In certain embodiments of the present invention, a batch of livecontent that had been collected for a predetermined period of time,typically a few minutes. All sentences in one batch may be processedtogether as a unit.

In block 340, the domain terminology expansion process 120 derivesecondary terms for applicable part-of-speech words. The domainterminology expansion process 120 tokenizes, parses, then performspart-of-speech (POS) tagging on the live content by use of the naturallanguage processing (NLP) components of the language modeling process170. As in typical NLP applications, the domain terminology expansionprocess 120 handles the words in the respective base forms referred toas lemmas. In one embodiment of the present invention, the applicablepart-of-speeches are noun and verb. The domain terminology expansionprocess 120 derives hypernyms and hyponyms for nouns, and synonyms forverbs, by use of synonym network tools of WordNet and VerbNet of theNatural Language Toolkit (NLTK) 159. Then the domain terminologyexpansion process 120 proceeds with block 350.

In block 350, the domain terminology expansion process 120 assessesrelevancies of each secondary terms derived in block 340, as perform inthe relevancy assessment of block 330. Then the domain terminologyexpansion process 120 proceeds with block 360.

In block 360, the domain terminology expansion process 120 performs theinclusion test of FIG. 4 by use of a logistic regression threshold todetermine whether or not to include the secondary terms in the corpus ofthe current domain. If the secondary term is relevant more than or equalto the logistic regression threshold, then the domain terminologyexpansion process 120 include the secondary term in the current domaincorpus by updating the current domain corpus. If relevancy of thesecondary term is less than the logistic regression threshold, then thedomain terminology expansion process 120 discards the secondary term andthe current domain corpus remains the same. Then the domain terminologyexpansion process 120 proceeds with block 370.

In block 370, the domain terminology expansion process 120 adds thesecondary terms derived in block 340 to the training data along with thecorresponding relevancy metric regardless of the result of the inclusiontest in block 360. One batch of the live content and the derivedsecondary terms are added as training data such that the training datawould encompass evidences that both support and negate the languagemodel of the current domain. Then the domain terminology expansionprocess 120 loops back to block 340 for the next sentence. Once theentire batch of the live content have been processed, then the domainterminology expansion process 120 proceeds with block 380.

In block 380, the domain terminology expansion process 120 trains thelanguage model of the current domain with the training data cumulatedfrom results of block 370 iterations. Then the domain terminologyexpansion process 120 proceeds with block 390.

In block 390, the domain terminology expansion process 120 evaluates thelanguage model of the current domain as expanded with the secondaryterms by use of conventional evaluation techniques such as ground truthtest of the language model measured by Word Error Rates (WER), etc. Thenthe domain terminology expansion process 120 loops back to block 320 toprocess a next batch of the live content.

FIG. 4 depicts a detailed flowchart of the inclusion test based onlogistic regression as performed in block 240 of FIG. 2 and block 360 ofFIG. 3, in accordance with one or more embodiments set forth herein.

In block 410, the domain terminology expansion process 120 calculateslogistic regression value sigma(t) for a sentence t, which takes anyreal input t, (tϵ

), and always results in values between zero (0) and one (1), which isinterpretable as a probability.

${\sigma(t)} = {\frac{e^{t}}{e^{t} + 1} = \frac{1}{1 + e^{- t}}}$

The sentence t may be the feature vectors passed from the externaldomains wherein the inclusion test is performed in block 240 of FIG. 2.The sentence t may be the live content crawled from the subject websiteswherein the inclusion test is performed in block 360 of FIG. 3. Then thedomain terminology expansion process 120 proceeds with block 420.

In block 420, the domain terminology expansion process 120 determineswhether or not the sigma(t) value calculated from block 410 is greaterthan or equal to a preconfigured threshold value to include the sentencet to the domain corpus. In certain embodiments of the present invention,the threshold value for the logistic regression may be configured asfifty percent (0.5=50%). If the domain terminology expansion process 120determines that the sigma(t) value from block 410 is greater than orequal to the preconfigured threshold value, then the domain terminologyexpansion process 120 proceeds with block 430. If the domain terminologyexpansion process 120 determines that the sigma(t) value from block 410is less than the preconfigured threshold value, then the domainterminology expansion process 120 proceeds with block 440.

In block 430, the domain terminology expansion process 120 includes thesentence tin the domain corpus of the current domain by updating thedomain corpus in the corpora 151. Then the domain terminology expansionprocess 120 loops back to next sentence subject to the inclusion test.If there is no more sentence to test for inclusion to the languagemodel, then the domain terminology expansion process 120 concludes theinclusion test and return to the caller process.

In block 440, the domain terminology expansion process 120 excludes thesentence t from the domain corpus of the current domain by discardingthe sentence t without updating the domain corpus in the corpora 151.Then the domain terminology expansion process 120 loops back to nextsentence subject to the inclusion test. If there is no more sentence totest for inclusion to the language model, then the domain terminologyexpansion process 120 concludes the inclusion test and return to thecaller process.

Certain embodiments of the present invention may offer various technicalcomputing advantages, including sharing of dynamic linguistic ontologiesamongst multiple domains to coerce secondary terms into related orunrelated dictionaries that are related to language models.Interrelating language models for multiple domains may provideadditional evidence with the current dynamic language model. Further,certain embodiments of the present invention cumulates positive andnegative evidences in including a word sequence into a neighboringlanguage model, and accordingly may expand the language model withprecision driven by individual relevancy to the subject domain. Certainembodiments of the present invention adapts a language model forautomatic speech recognition (ASR) or other natural languageapplications more accurately and more comprehensively than conventionaldecision based on semantic redundancy and/or ontological alignment ofwords, particularly with respect to disambiguation of word sequences andpatterns, and accordingly may contribute in improving performances ofthe ASR and other natural language processing applications.

FIGS. 5-7 depict various aspects of computing, including a computersystem and cloud computing, in accordance with one or more aspects setforth herein.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, a schematic of an example of a computersystem/cloud computing node is shown. Cloud computing node 10 is onlyone example of a suitable cloud computing node and is not intended tosuggest any limitation as to the scope of use or functionality ofembodiments of the invention described herein. Regardless, cloudcomputing node 10 is capable of being implemented and/or performing anyof the functionality set forth hereinabove.

In cloud computing node 10 there is a computer system 12, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system 12 include, but are not limitedto, personal computer systems, server computer systems, thin clients,thick clients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system 12 may be described in the general context of computersystem-executable instructions, such as program processes, beingexecuted by a computer system. Generally, program processes may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program processes may belocated in both local and remote computer system storage media includingmemory storage devices.

As shown in FIG. 5, computer system 12 in cloud computing node 10 isshown in the form of a general-purpose computing device. The componentsof computer system 12 may include, but are not limited to, one or moreprocessors 16, a system memory 28, and a bus 18 that couples varioussystem components including system memory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system 12 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system 12, and it includes both volatile and non-volatilemedia, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program processes that are configured to carry out thefunctions of embodiments of the invention.

One or more program 40, having a set (at least one) of program processes42, may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram processes, and program data. Each of the operating system, oneor more application programs, other program processes, and program dataor some combination thereof, may include an implementation of the domainterminology expansion process 120 of FIG. 1. Program processes 42, as inthe domain terminology expansion process 120 generally carry out thefunctions and/or methodologies of embodiments of the invention asdescribed herein.

Computer system 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computer system12; and/or any devices (e.g., network card, modem, etc.) that enablecomputer system 12 to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O) interfaces22. Still yet, computer system 12 can communicate with one or morenetworks such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter20. As depicted, network adapter 20 communicates with the othercomponents of computer system 12 via bus 18. It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with computer system 12. Examples, include, butare not limited to: microcode, device drivers, redundant processors,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

Referring now to FIG. 6, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 7, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and processing components for the domainterminology expansion process 96, as described herein. The processingcomponents 96 can be understood as one or more program 40 described inFIG. 5.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprise” (and any form ofcomprise, such as “comprises” and “comprising”), “have” (and any form ofhave, such as “has” and “having”), “include” (and any form of include,such as “includes” and “including”), and “contain” (and any form ofcontain, such as “contains” and “containing”) are open-ended linkingverbs. As a result, a method or device that “comprises,” “has,”“includes,” or “contains” one or more steps or elements possesses thoseone or more steps or elements, but is not limited to possessing onlythose one or more steps or elements. Likewise, a step of a method or anelement of a device that “comprises,” “has,” “includes,” or “contains”one or more features possesses those one or more features, but is notlimited to possessing only those one or more features. Furthermore, adevice or structure that is configured in a certain way is configured inat least that way, but may also be configured in ways that are notlisted.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below, if any, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description set forth herein has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of one or more aspects set forth herein and the practicalapplication, and to enable others of ordinary skill in the art tounderstand one or more aspects as described herein for variousembodiments with various modifications as are suited to the particularuse contemplated.

What is claimed is:
 1. A computer implemented method for expanding alanguage model corresponding to a domain, comprising: collecting, by oneor more processor, at least one feature vector from one or more externaldomain distinctive from the domain, wherein the domain and the one ormore external domain are interconnected via a cloud; expanding thelanguage model, stored in a corpora coupled to the cloud, with a featurevector of the at least one feature vector from the collecting, whereinthe feature vector is, based on a logistic regression threshold, morerelevant to the domain than not, the expanding comprising: (i)calculating a logistic regression value for the feature vector by use ofσ(t)=e^(t)/(e^(t)+1)=1/(1+e^(−t)), wherein σ(t) indicates a standardequation of logistic regression for sentence t that represents thefeature vector, (ii) ascertaining that the logistic regression value forthe feature vector indicating that the probability of the feature vectorto be relevant to the domain is greater than or equal to the logisticregression threshold of zero point five (0.5); and (iii) updating thelanguage model in the corpora by adding the feature vector from theascertaining to the language model; enhancing the language model bymachine learning live content from one or more subject website in whichthe domain is interested such that the language model includes the livecontent and one or more secondary term derived from the live contentthat are more relevant to the domain than not, such that the languagemodel accurately and comprehensively facilitates an automatic speechrecognition (ASR) system for the domain; and performing speechrecognition on a received speech input utilizing at least the enhancedlanguage model.
 2. The computer implemented method of claim 1, theenhancing comprising: crawling the live content from the one or moresubject website; parsing, tokenizing and tagging part-of-speech eachword sequence of the live content, responsive to determining that thelanguage of the live content is supported and that the live content isgrammatically correct; assessing relevancy of each word sequence of thelive content to the domain, wherein each word sequence includes one ormore word; deriving the one or more secondary term from each wordsequence of the live content; assessing relevancy of each secondary termfrom the deriving; and expanding the language model stored in thecorpora with each secondary term that is more relevant to the domainthan not, based on respective logistic regression values of eachsecondary term.
 3. The computer implemented method of claim 2, whereinthe one or more secondary term is selected from a hypernym and/or ahyponym for a noun, and a synonym for a verb.
 4. The computerimplemented method of claim 1, further comprising: adding the at leastone feature vector, one or more word of the live content, and the one ormore secondary term, either more relevant or less relevant to thedomain, to training data for the language model such that the trainingdata may encompass evidences both supporting and negating the languagemodel for accuracy; and training the language model by building groundtruths with the training data.
 5. The computer implemented method ofclaim 1, further comprising: evaluating the language model from theexpanding and from the enhancing, by use of evaluation techniquesselected from Word Error Rate (WER), a ground truth test, andcombinations thereof.
 6. The computer implemented method of claim 1,wherein the assessing relevancy is performed by IBM Watson Retrieve andRank service, wherein the deriving is performed by the Natural LanguageToolkit (NLTK) including WordNet and VerbNet, and wherein lexicalanalysis and other natural language processing (NLP) are implemented byuse of IBM Watson NLP tools including IBM Watson Language Translatorservice, IBM Watson Speech-to-Text service and the corpora isimplemented by use of IBM dashDB.
 7. The computer implemented method ofclaim 1, further comprising: utilizing, by the ASR system for thedomain, the language model from the expanding and the enhancing indomain-specific disambiguation and recognition of terminologies used inthe domain.
 8. A computer program product comprising: a non-transitorycomputer readable storage medium readable by one or more processor andstoring instructions for execution by the one or more processor forperforming a method for expanding a language model corresponding to adomain, comprising: collecting at least one feature vector from one ormore external domain distinctive from the domain, wherein the domain andthe one or more external domain are interconnected via a cloud;expanding the language model, stored in a corpora coupled to the cloud,with a feature vector of the at least one feature vector from thecollecting, wherein the feature vector is, based on a logisticregression threshold, more relevant to the domain than not, theexpanding comprising: (i) calculating a logistic regression value forthe feature vector by use of σ(t)=e^(t)/(e^(t)+1)=1/(1+e^(−t)), whereinσ(t) indicates a standard equation of logistic regression for sentence tthat represents the feature vector, (ii) ascertaining that the logisticregression value for the feature vector indicating that the probabilityof the feature vector to be relevant to the domain is greater than orequal to the logistic regression threshold of zero point five (0.5); and(iii) updating the language model in the corpora by adding the featurevector from the ascertaining to the language model; enhancing thelanguage model by machine learning live content from one or more subjectwebsite in which the domain is interested such that the language modelincludes the live content and one or more secondary term derived fromthe live content that are more relevant to the domain than not, suchthat the language model accurately and comprehensively facilitates anautomatic speech recognition (ASR) system for the domain; and performingspeech recognition on a received speech input utilizing at least theenhanced language model.
 9. The computer program product of claim 8, theenhancing comprising: crawling the live content from the one or moresubject website; parsing, tokenizing and tagging part-of-speech eachword sequence of the live content, responsive to determining that thelanguage of the live content is supported and that the live content isgrammatically correct; assessing relevancy of each word sequence of thelive content to the domain, wherein each word sequence includes one ormore word; deriving the one or more secondary term from each wordsequence of the live content; assessing relevancy of each secondary termfrom the deriving; and expanding the language model stored in thecorpora with each secondary term that is more relevant to the domainthan not, based on respective logistic regression values of eachsecondary term.
 10. The computer program product of claim 9, wherein theone or more secondary term is selected from a hypernym and/or a hyponymfor a noun, and a synonym for a verb.
 11. The computer program productof claim 8, further comprising: adding the at least one feature vector,one or more word of the live content, and the one or more secondaryterm, either more relevant or less relevant to the domain, to trainingdata for the language model such that the training data may encompassevidences both supporting and negating the language model for accuracy;and training the language model by building ground truths with thetraining data.
 12. The computer program product of claim 8, furthercomprising: evaluating the language model from the expanding and fromthe enhancing, by use of evaluation techniques selected from Word ErrorRate (WER), a ground truth test, and combinations thereof.
 13. Thecomputer program product of claim 8, wherein the assessing relevancy isperformed by IBM Watson Retrieve and Rank service, wherein the derivingis performed by the Natural Language Toolkit (NLTK) including WordNetand VerbNet, and wherein lexical analysis and other natural languageprocessing (NLP) are implemented by use of IBM Watson NLP toolsincluding IBM Watson Language Translator service, IBM WatsonSpeech-to-Text service and the corpora is implemented by use of IBMdashDB.
 14. The computer program product of claim 8, further comprising:utilizing, by the ASR system for the domain, the language model from theexpanding and the enhancing in domain-specific disambiguation andrecognition of terminologies used in the domain.
 15. A systemcomprising: a memory; one or more processor in communication withmemory; and program instructions executable by the one or more processorvia the memory to perform a method for expanding a language modelcorresponding to a domain, comprising: collecting at least one featurevector from one or more external domain distinctive from the domain,wherein the domain and the one or more external domain areinterconnected via a cloud; expanding the language model, stored in acorpora coupled to the cloud, with a feature vector of the at least onefeature vector from the collecting, wherein the feature vector is, basedon a logistic regression threshold, more relevant to the domain thannot, the expanding comprising: (i) calculating a logistic regressionvalue for the feature vector by use of σ(t)=e^(t)/(e^(t)+1)=1/(1+e^(t)),wherein σ(t) indicates a standard equation of logistic regression forsentence t that represents the feature vector, (ii) ascertaining thatthe logistic regression value for the feature vector indicating that theprobability of the feature vector to be relevant to the domain isgreater than or equal to the logistic regression threshold of zero pointfive (0.5); and (iii) updating the language model in the corpora byadding the feature vector from the ascertaining to the language model;enhancing the language model by machine learning live content from oneor more subject website in which the domain is interested such that thelanguage model includes the live content and one or more secondary termderived from the live content that are more relevant to the domain thannot, such that the language model accurately and comprehensivelyfacilitates an automatic speech recognition (ASR) system for the domain;and performing speech recognition on a received speech input utilizingat least the enhanced language model.
 16. The system of claim 15, theenhancing comprising: crawling the live content from the one or moresubject website; parsing, tokenizing and tagging part-of-speech eachword sequence of the live content, responsive to determining that thelanguage of the live content is supported and that the live content isgrammatically correct; assessing relevancy of each word sequence of thelive content to the domain, wherein each word sequence includes one ormore word; deriving the one or more secondary term from each wordsequence of the live content; assessing relevancy of each secondary termfrom the deriving; and expanding the language model stored in thecorpora with each secondary term that is more relevant to the domainthan not, based on respective logistic regression values of eachsecondary term.
 17. The system of claim 16, wherein the one or moresecondary term is selected from a hypernym and/or a hyponym for a noun,and a synonym for a verb.
 18. The system of claim 15, furthercomprising: adding the at least one feature vector, one or more word ofthe live content, and the one or more secondary term, either morerelevant or less relevant to the domain, to training data for thelanguage model such that the training data may encompass evidences bothsupporting and negating the language model for accuracy; and trainingthe language model by building ground truths with the training data. 19.The system of claim 15, further comprising: evaluating the languagemodel from the expanding and from the enhancing, by use of evaluationtechniques selected from Word Error Rate (WER), a ground truth test, andcombinations thereof.
 20. The system of claim 15, further comprising:utilizing, by the ASR system for the domain, the language model from theexpanding and the enhancing in domain-specific disambiguation andrecognition of terminologies used in the domain.